Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Détails

ID Serval
serval:BIB_853A933D3447
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Titre
Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning
Titre du livre
Computational Vision and Medical Image Processing
Auteur(s)
Bach Cuadra  Meritxell, Gorthi  Subrahmanyam, Karahanoglu  Fikret Isik, Paquier  Benoît, Pica  Alessia, Do  Huu Phuoc, Balmer  Aubain, Munier  Francis, Thiran  Jean-Philippe
Editeur
Springer Netherlands
Lieu d'édition
Dordrecht; New York
ISBN
9789400700109
Statut éditorial
Publié
Date de publication
2011
Editeur scientifique
Tavares  João Manuel R. S., Jorge  R. M. Natal
Volume
19
Série
Computational Methods in Applied Sciences
Pages
247-263
Langue
anglais
Résumé
Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.
Mots-clé
Parametric Active Contours , Model-based segmentation , Multi-model image fusion , Ultrasound imaging , Computer tomography , Eye imaging , radiotherapy
Création de la notice
29/02/2012 18:12
Dernière modification de la notice
20/08/2019 14:44
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